
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					str     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					int=13     ,				UpperCamelCase__     :					int=30     ,				UpperCamelCase__     :					Dict=2     ,				UpperCamelCase__     :					Tuple=3     ,				UpperCamelCase__     :					Dict=True     ,				UpperCamelCase__     :					Optional[int]=True     ,				UpperCamelCase__     :					int=32     ,				UpperCamelCase__     :					Optional[int]=5     ,				UpperCamelCase__     :					Tuple=4     ,				UpperCamelCase__     :					int=37     ,				UpperCamelCase__     :					Optional[int]="gelu"     ,				UpperCamelCase__     :					List[Any]=0.1     ,				UpperCamelCase__     :					Any=0.1     ,				UpperCamelCase__     :					Optional[int]=10     ,				UpperCamelCase__     :					Union[str, Any]=0.02     ,				UpperCamelCase__     :					Any=None     ,				)     ->      Any:







    """simple docstring"""






    __magic_name__								=			parent
    __magic_name__								=			batch_size
    __magic_name__								=			image_size
    __magic_name__								=			patch_size
    __magic_name__								=			num_channels
    __magic_name__								=			is_training
    __magic_name__								=			use_labels
    __magic_name__								=			hidden_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			intermediate_size
    __magic_name__								=			hidden_act
    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			type_sequence_label_size
    __magic_name__								=			initializer_range
    __magic_name__								=			scope

    # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    __magic_name__								=			(image_size // patch_size) ** 2
    __magic_name__								=			num_patches + 1
  def 				_lowercase				(					self     :					int       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

    __magic_name__								=			None
    if self.use_labels:
      __magic_name__								=			ids_tensor([self.batch_size]     ,				self.type_sequence_label_size       )

    __magic_name__								=			self.get_config()

    return config, pixel_values, labels
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[int]:







    """simple docstring"""






    return ViTMSNConfig(
        image_size=self.image_size     ,				patch_size=self.patch_size     ,				num_channels=self.num_channels     ,				hidden_size=self.hidden_size     ,				num_hidden_layers=self.num_hidden_layers     ,				num_attention_heads=self.num_attention_heads     ,				intermediate_size=self.intermediate_size     ,				hidden_act=self.hidden_act     ,				hidden_dropout_prob=self.hidden_dropout_prob     ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,				initializer_range=self.initializer_range     ,				)
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					List[Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			ViTMSNModel(config=UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			model(UpperCamelCase__       )
    self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size)       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.type_sequence_label_size
    __magic_name__								=			ViTMSNForImageClassification(UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			model(UpperCamelCase__     ,				labels=UpperCamelCase__       )
    print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""       )
    print("""Labels: {labels}"""       )
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size)       )

    # test greyscale images
    __magic_name__								=			1
    __magic_name__								=			ViTMSNForImageClassification(UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()

    __magic_name__								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
    __magic_name__								=			model(UpperCamelCase__       )
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size)       )





  def 				_lowercase				(					self     :					Any       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.prepare_config_and_inputs()
    __magic_name__	,       __magic_name__	,       __magic_name__								=			config_and_inputs
    __magic_name__								=			{"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_torch
class 			UpperCAmelCase_     (					_A    ,			_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
  a__       			=						(
      {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
      if is_torch_available()
      else {}
  )

  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						False
  def 				_lowercase				(					self     :					Any       )     ->      str:







    """simple docstring"""






    __magic_name__								=			ViTMSNModelTester(self       )
    __magic_name__								=			ConfigTester(self     ,				config_class=UpperCamelCase__     ,				has_text_modality=UpperCamelCase__     ,				hidden_size=37       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[int]:







    """simple docstring"""






    self.config_tester.run_common_tests()
  @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      self.assertIsInstance(model.get_input_embeddings()     ,				(nn.Module)       )
      __magic_name__								=			model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(UpperCamelCase__     ,				nn.Linear       )       )
  def 				_lowercase				(					self     :					Dict       )     ->      List[str]:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      __magic_name__								=			inspect.signature(model.forward       )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      __magic_name__								=			[*signature.parameters.keys()]

      __magic_name__								=			["""pixel_values"""]
      self.assertListEqual(arg_names[:1]     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					str       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__       )





  @slow
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      List[str]:







    """simple docstring"""






    for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __magic_name__								=			ViTMSNModel.from_pretrained(UpperCamelCase__       )
      self.assertIsNotNone(UpperCamelCase__       )







def a__       (			):






  '''simple docstring'''





  __magic_name__								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
  return image





@require_torch
@require_vision
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  @cached_property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""       ) if is_vision_available() else None





  @slow
  def 				_lowercase				(					self     :					Any       )     ->      List[Any]:







    """simple docstring"""






    torch.manual_seed(2       )
    __magic_name__								=			ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""       ).to(UpperCamelCase__       )

    __magic_name__								=			self.default_image_processor
    __magic_name__								=			prepare_img()
    __magic_name__								=			image_processor(images=UpperCamelCase__     ,				return_tensors="""pt"""       ).to(UpperCamelCase__       )

    # forward pass
    with torch.no_grad():
      __magic_name__								=			model(**UpperCamelCase__       )

    # verify the logits
    __magic_name__								=			torch.Size((1, 1000)       )
    self.assertEqual(outputs.logits.shape     ,				UpperCamelCase__       )

    __magic_name__								=			torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(UpperCamelCase__       )

    self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,				UpperCamelCase__     ,				atol=1E-4       )       )



def a__       (			A_   ):






  '''simple docstring'''





  if len(A_   ) < 2:
    return collection

  def circle_sort_util(A_,	A_,	A_   ) -> bool:
    __magic_name__								=			False

    if low == high:
      return swapped

    __magic_name__								=			low
    __magic_name__								=			high

    while left < right:
      if collection[left] > collection[right]:
        __magic_name__	,       __magic_name__								=			(
            collection[right],
            collection[left],
        )
        __magic_name__								=			True

      left += 1
      right -= 1

    if left == right and collection[left] > collection[right + 1]:
      __magic_name__	,       __magic_name__								=			(
          collection[right + 1],
          collection[left],
      )

      __magic_name__								=			True

    __magic_name__								=			low + int((high - low) / 2   )
    __magic_name__								=			circle_sort_util(A_,	A_,	A_   )
    __magic_name__								=			circle_sort_util(A_,	mid + 1,	A_   )

    return swapped or left_swap or right_swap

  __magic_name__								=			True

  while is_not_sorted is True:
    __magic_name__								=			circle_sort_util(A_,	0,	len(A_   ) - 1   )

  return collection


if __name__ == "__main__":
    __lowerCAmelCase     :	Union[str, Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	Tuple       			=  [int(item) for item in user_input.split(',')]
    print(circle_sort(unsorted))

